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Erschienen in: Reproductive Biology and Endocrinology 1/2007

Open Access 01.12.2007 | Research

Genomic and proteomic profiling II: Comparative assessment of gene expression profiles in leiomyomas, keloids, and surgically-induced scars

verfasst von: Xiaoping Luo, Qun Pan, Li Liu, Nasser Chegini

Erschienen in: Reproductive Biology and Endocrinology | Ausgabe 1/2007

Abstract

Background

Leiomyoma have often been compared to keloids because of their fibrotic characteristic and higher rate of occurrence among African Americans as compared to other ethnic groups. To evaluate such a correlation at molecular level this study comparatively analyzed leiomyomas with keloids, surgical scars and peritoneal adhesions to identify genes that are either commonly and/or individually distinguish these fibrotic disorders despite differences in the nature of their development and growth.

Methods

Microarray gene expression profiling and realtime PCR.

Results

The analysis identified 3 to 12% of the genes on the arrays as differentially expressed among these tissues based on P ranking at greater than or equal to 0.005 followed by 2-fold cutoff change selection. Of these genes about 400 genes were identified as differentially expressed in leiomyomas as compared to keloids/incisional scars, and 85 genes as compared to peritoneal adhesions (greater than or equal to 0.01). Functional analysis indicated that the majority of these genes serve as regulators of cell growth (cell cycle/apoptosis), tissue turnover, transcription factors and signal transduction. Of these genes the expression of E2F1, RUNX3, EGR3, TBPIP, ECM-2, ESM1, THBS1, GAS1, ADAM17, CST6, FBLN5, and COL18A was confirmed in these tissues using quantitative realtime PCR based on low-density arrays.

Conclusion

the results indicated that the molecular feature of leiomyomas is comparable but may be under different tissue-specific regulatory control to those of keloids and differ at the levels rather than tissue-specific expression of selected number of genes functionally regulating cell growth and apoptosis, inflammation, angiogenesis and tissue turnover.
Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1477-7827-5-35) contains supplementary material, which is available to authorized users.

Competing interests

The author(s) declare that they have no competing interests.

Authors' contributions

XL, QP and NC participated in all aspect of the experimental design and writing of the work presented here. The final microarray gene chips were performed at Interdisciplinary Center for Biotechnology Research at the University of Florida. The analysis of microarray gene expression profiles between the gene chips U95 and 133a was carried out by LL and gene expression analysis and realtime PCR was performed by XL and QP. All the authors read and approved the final manuscript.

Background

Leiomyomas are benign uterine tumors with unknown etiology that originate from transformation of myometrial smooth muscle cells and/or connective tissue fibroblasts during the reproductive years. Leiomyomas can develop in multiple numbers that are individually encapsulated by a connective tissue core separating them from the surrounding normal myometrium and are ovarian steroid-dependent for their growth. Although they occur independent of ethnicity, clinical and epidemiological studies have indicated that African Americans are at a higher risk of developing leiomyomas compared to other ethnic groups [1].
Leiomyomas have also often been compared to keloids because of a higher rate of occurrence in African Americans and their fibrotic characteristics despite differences in the nature of their development and growth [2]. Keloids are benign skin lesions that develop spontaneously, or form from proliferation of dermal cells following tissue injury resulting in a collagenous and poorly vascularized structure at later stage of their development [36]. Unlike surgically-induced and hypertrophic scars that are confined to the area of original tissue injury, keloids can expand beyond the boundaries of their original sites following removal and during healing. Keloids are rather similar to hypertrophic scars at early stages of development, however they become collagenous and poorly vascularized at later stages and tend to occur more frequently in darker skinned individuals [3, 4]. Surgically-induced injury and/or inflammation also result in peritoneal scar or adhesions and similar to other incisional scars they are confined to the area of tissue injury[7]. Peritoneal adhesions also display a considerable histological similarity with dermal scars; however there is no data to suggest a higher risk of adhesion formation with ethnicity. Comparatively, uterine tissue injury i.e., following myomectomy or cesarean sections, does not cause leiomyomas formation, but rather results in incisional scar formation at the site of injury. Furthermore, leiomyomas consist mainly of smooth muscle cells forming a relatively vascuraized tissue, while keloids derive from proliferation of connective tissue fibroblasts, adopting a myofibroblastic phenotype at a later stage of wound healing[3, 4].
As part of these characteristics previous studies have identified excess production and deposition of extracellular matrix, namely collagens in leiomyomas, keloids, hypertrophic and surgical scars and peritoneal adhesions [2, 710]. Evidence also exists implicating altered production of several proinflammatory and profibrotic cytokines, proteases and adhesion molecules in pathogenesis and characteristic of these and other fibrotic disorders [1114]. Large-scale gene expression studies have provided additional evidence for the expression of a number of differentially expressed genes in leiomyomas [11, 1517], keloids and hypertrophic scars [15, 16] as compared to their respective normal tissues. Several conventional studies have demonstrated that the products of some of these genes regulate various cellular activities implicated in the outcome of tissue fibrosis at various sites throughout the body Among these genes, include several growth factors and cytokines such as TGF-β system, proteases, adhesion molecules and extracellular matrix etc. (for review see [717]). Despite these advancements, the biological significance of many of these genes in pathophysiology of leiomyomas and keloids and their relationship to the outcome of other tissue fibrosis remains to be established. In addition, there has not been any study that comparatively analyzed the molecular profile that distinguishes leiomyomas from other fibrotic tissues, specifically keloids.
Considering these characteristics we used large-scale gene expression profiling to evaluate such a correlation at molecular level by comparatively analyzing leiomyomas with keloids, surgical scars and peritoneal adhesions to identify genes that are either commonly and/or individually distinguish these fibrotic disorders despite differences in nature of their development and growth. We evaluated the expression of 12 genes in these tissues representing several functional categories important to tissue fibrosis using quantitative realtime PCR based on low-density arrays.

Methods

All the materials and methods utilized in this study are identical to our previous studies and those reported in the accompanying manuscript [11, 17]. Prior approval was obtained from the University of Florida Institutional Review Board for the experimental protocol of this study, with patients with scars giving informed consent, while the study with leiomyomas was expedited and did require obtaining written informed consent.
Total cellular RNA was isolated from keloid/incisional scars (N = 4) and subjected to microarray analysis using human U133A Affymetrix GeneChips as described in the accompanying manuscript [17]. One patient who had developed keloid at the site of previous surgical incision also developed leiomyoma. All the patients with keloids and one patient with incisional scar were African Americans. In addition, we utilized the gene expression data obtained from our previous study [11] involving leiomyomas (N = 3) and peritoneal adhesions (N = 3) using human U95A GeneChips. These tissues were from Caucasians patients with the exception of one peritoneal adhesion collected from an African American patient. The age of patients with leiomyomas ranged from 29 to 38 years. These women were not taking any medication, including hormonal therapy, for pervious 3 months prior to surgery and based on their last menstrual period and endometrial histology was from early-mid secretary phase of the menstrual cycle. The age of patients with adhesions ranged from 25 to 46 years and those with keloids and surgical scars were 26, 32 and 39 years, respectively. All the tissues with the exception of one keloid matched by their corresponding normal tissues i.e. myometrium, skin and parietal peritoneum for microarry analysis. All the procedures for total RNA isolation, amplification, cDNA synthesis, RNA labeling and hybridization into the GeneChips were carried out as previously described in detail [11].

Microarray data analysis

The gene expression values obtained from the leiomyomas and matched myometrium (N = 6) using U133A GeneChips in the accompanying manuscript was utilized here only for the purpose of comparative analysis. The gene expression values obtained from all U133A and U95A GeneChips were independently subjected to global normalization and transformation, and their coefficient of variation was calculated for each probe set across the chips as previously described [11]. The selected gene expression values were than subjected to supervised learning including statistical analysis in R programming and ANOVA with Turkey test and gene ranking at P ≤ 0.005 followed by 2-fold change cutoff[11]. Functional annotation and molecular pathway analysis was carried out as described [17].
For combining the data from the U95A and U133A chips the probes that were absent across all chips were removed and subjected to t-test to identify differentially expressed genes. The data set was annotated using Entrez Gene and full annotation files NetAffy software and probe sets were consolidated based on Entrez Gene ID and subjected to microarray.dog.MetaAnalysisTester. The analysis keeps one probe for each gene with the smallest p-value for up or down t-test. The probe with smallest p-value for up regulated genes may be different from probe sets with smallest p-value for down-regulated genes. When the data from U95A and U133A was combined if a gene was represented on one platform, but not on both the missing data was replaced with NA. The data was subjected to Fisher combine p-values using inverse chi-square method and permutation test to determine new p-value, named randomized inverse chi-square p-value and to calculate the traditional inverse chi-square p-value. The false discovery rate was calculated using the inverse chi-square p-value and the min t-test p-value for each gene.

Quantitative realtime PCR

The same total RNA isolated from these tissues and used for microarray studies was also subjected to quantitative realtime PCR using custom-made TaqMan Low Density Arrays (LDAs) assessing the expression of 12 genes and the house-keeping gene, GAPDH. Detailed descriptions of LDA and realtime PCR, including data analysis has been provided in the accompanied manuscript[17].

Results

Gene expression profiles of leiomyomas, keloids and scars

Utilizing Affymetrix U133A platform we first assessed the gene expression profile of keloids and incisional scars. Following supervised and unsupervised assessments of the gene expression values in each cohort the combined data set with the gene expression values of leiomyomas reported in the accompanying manuscript using U133A arrays [17] only for the purpose of comparative analysis. The analysis based on supervised and unsupervised assessment and P ranking of P < 0.005, followed by 2-fold cutoff change selection, resulted in identification of 1124 transcripts (1103 genes) of which 732 genes were over-expressed and 371 were under-expressed in leiomyomas as compared to keloids/incisional scars (N = 4). Hierarchical clustering separated these genes into distinctive groups with each cohort clustering into the corresponding subgroup (Fig. 1). A partial list of these differentially expressed genes with their biological functions is shown in Tables 1 and 2. The combined gene list presented in Tables 1 and 2 is different from the list reported in the accompanying manuscript for leiomyomas[17], although many commonly expressed genes displaying different expression values could be find in between the tables.
Table 1
List of over-expressed in leiomyomas as compared to scar tissues (keloids/incesional scars)
Gene Bank
Symbol
Fold Change
Probability
Function
NM_003478
CUL5
5.06
0.0001
apoptosis
AB037736
CASP8AP2
4.07
0.0021
apoptosis
NM_018947
CYCS
2.08
0.0013
apoptosis
AB014517
CUL3
2.07
0.00001
apoptosis
BC010958
CCND2
5.62
0.0041
cell cycle
U47413
CCNG1
3.16
0.0007
cell cycle
AF048731
CCNT2
2.83
0.0004
cell cycle
NM_001927
DBS
61.51
0.0022
cytoskeleton/motility
AK124338
ACTG2
30.16
0.00001
cytoskeleton/motility
BC022015
CNN1
27.26
0.00001
cytoskeleton/motility
NM_006449
CDC42EP3
25.29
0.0051
cytoskeleton/motility
AB023209
KIAA0992
17.61
0.0004
cytoskeleton/motility
AF474156
TPM1
14.84
0.0029
cytoskeleton/motility
BC011776
TPM2
12.04
0.00001
cytoskeleton/motility
M11315
COL4A1
11.87
0.0029
cytoskeleton/motility
AK126474
LMOD1
9.49
0.00001
cytoskeleton/motility
AB062484
CALD1
9.22
0.0042
cytoskeleton/motility
NM_003186
TAGLN
6.68
0.00001
cytoskeleton/motility
BC017554
ACTA2
5.18
0.00001
cytoskeleton/motility
AK074048
FLNA
5.08
0.00001
cytoskeleton/motility
NM_016274
CKIP-1
4.44
0.002
cytoskeleton/motility
BC003576
ACTN1
4.23
0.0024
cytoskeleton/motility
AF089841
FLNC
3.43
0.0005
cytoskeleton/motility
X05610
COL4A2
7.86
0.0017
extracellular matrix
BC005159
COL6A1
3.70
0.002
extracellular matrix
A98730
CAPN6
13.7
0.0023
protease activity
U41766
ADAM9
4.76
0.0021
protease
NM_001110
ADAM10
3.2
0.00001
protease
AF031385
CYR61 (CCN1)
9.13
0.0035
growth factor
M32977
VEGF
7.13
0.002
growth factor
AF035287
SDFR1
4.70
0.0001
chemokine receptor
X04434
IGF1R
3.64
0.0017
growth factor receptor
AB029156
HDGFRP3
2.89
0.0006
GF receptor activity
AF056979
IFNGR1
2.72
0.0001
signal transduction
AB020673
MYH11
53.80
0.0006
signal transduction
D26070
ITPR1
26.18
0.0034
signal transduction
AB037717
SORBS1
15.25
0.0005
signal transduction
AF110225
ITGB1BP2
14.18
0.0009
signal transduction
AB004903
SOCS2
11.39
0.0002
signal transduction
B011147
GREB1
11.37
0.0025
signal transduction
AB000509
TRAF5
7.83
0.0032
signal transduction
NM_005261
GEM
7.48
0.0003
signal transduction
AF028832
HSPCA
4.27
0.00001
signal transduction
AC006581
M6PR
3.85
0.0012
signal transduction
AF275719
HSPCB
3.74
0.001
signal transduction
AJ242780
ITPKB
3.68
0.00001
signal transduction
AK095866
GPR125
3.62
0.0001
signal transduction
AF016050
NRP1
3.44
0.0011
signal transduction
AB015706
IL6ST
3.42
0.0002
signal transduction
AK057120
HMGB1
3.16
0.0001
signal transduction
NM_006644
HSPH1
3.14
0.002
signal transduction
AB072923
BSG
2.90
0.0024
signal transduction
AB010881
FZD7
2.62
0.0024
signal transduction
AF273055
INPP5A
2.58
0.002
signal transduction
AC078943
TANK
2.32
0.0005
signal transduction
AF051344
LTBP4
2.20
0.0002
signal transduction
AJ404847
ILK
4.74
0.0002
protein kinase activity
AF119911
CSNK1A1
3.40
0.0015
protein kinase activity
NM_002037
FYN
3.30
0.0028
protein kinase activity
AB058694
CDC2L5
2.37
0.0001
protein kinase activity
AF415177
CAMK2G
2.18
0.0008
protein kinase activity
NM_005654
NR2F1
12.57
0.0039
transcription factor
BC062602
PNN
9.93
0.0001
transcription factor
AK098174
MEIS1
9.61
0.00001
transcription factor
NM_000125
ESR1
9.36
0.0004
transcription factor
AF249273
BCLAF1
8.62
0.0001
transcription factor
AF017418
MEIS2
7.46
0.0009
transcription factor
AF045447
MADH4
6.39
0.00001
transcription factor
AF162704
AR
5.54
0.0018
transcription factor
NM_001527
HDAC2
4.76
0.00001
transcription factor
NM_004268
CRSP6
4.76
0.0001
transcription factor
BC020868
STAT5B
4.57
0.0003
transcription factor
BC002646
JUN
3.84
0.0042
transcription factor
AY347527
CREB1
3.77
0.0031
transcription factor
AL833643
MAX
3.66
0.0014
transcription factor
NM_021809
TGIF2
3.58
0.0014
transcription factor
AB007836
TGFB1I1
3.55
0.0007
transcription coactivator
NM_005760
CEBPZ
3.53
0.00001
transcription factor
AL833268
MEF2C
3.49
0.0019
transcription factor
NM_005903
MADH5
3.10
0.0037
transcription factor
NM_022739
SMURF2
2.58
0.0013
transcription factor
NM_003472
DEK
2.55
0.0001
transcription factor
NM_001358
DHX15
2.49
0.0029
transcription factor
BC029619
ATF1
2.41
0.0026
transcription factor
AB082525
TSC22
2.26
0.0002
transcription factor
AL831995
MEF2A
2.25
0.0024
transcription factor
AA765457
DDX17
10.41
0.0035
translation factor
NM_018951
HOXA10
8.69
0.00001
translation factor
BC000751
EIF5A
4.07
0.001
translation factor
AF015812
DDX5
2.48
0.0004
translation factor
AL079283
EIF1A
2.35
0.0005
translation factor
NM_003760
EIF4G3
2.35
0.0028
translation factor
NM_012218
ILF3
2.29
0.0003
translation factor
AB018284
EIF5B
2.26
0.002
translation factor
AF155908
HSPB7
9.52
0.0002
protein binding
AF209712
MCP
6.54
0.00001
complement activation
AL833430
SPARCL1
5.12
0.00001
calcium ion binding
AF297048
PTGIS
4.26
0.0004
catalytic activity
AF288537
FSTL1
4.11
0.001
calcium ion binding
AB034951
HSPA8
3.13
0.001
protein binding
NM_001155
ANXA6
2.85
0.0014
calcium ion binding
NM_003642
HAT1
2.81
0.00001
catalytic activity
NM_002267
KPNA3
2.55
0.0031
protein transporter
AK124769
XPO1
2.46
0.0002
protein transporter
AJ238248
CENTB2
2.37
0.0045
GTPase activator activity
AF072928
MTMR6
2.17
0.002
phosphatase activity
Partial list of differentially expressed genes identified in leiomyomas (African Americans and Caucasians) as compared to keloid/incisional scars as shown in Fig. 1. The genes were selected based on p ranking of p ≤ 0.005 and 2-fold cutoff change selection (F. Change) as described in materials and methods. Table 1 displays the over-expressed genes in leiomyomas as compared to keloid/incisional scars.
Table 2
List of under-expressed in leiomyomas as compared to scar tissues (keloids/incesional scars)
Gene Bank
Symbol
Fold Change
Probability
Function
AF004709
MAPK13
0.06
0.0002
apoptosis
AF010316
PTGES
0.09
0.0003
apoptosis
NM_014430
CIDEB
0.21
0.0014
apoptosis
AJ307882
TRADD
0.26
0.0007
apoptosis
BC041689
CASP1
0.31
0.0009
apoptosis
NM_014922
NALP1
0.31
0.0025
apoptosis
AF159615
FRAG1
0.33
0.0044
apoptosis
BC019307
BCL2L1
0.42
0.0027
apoptosis
NM_016426
GTSE1
0.43
0.0033
apoptosis
AK027080
LTBR
0.50
0.0047
apoptosis
M92287
CCND3
0.48
0.0028
cell cycle
AJ242501
MAP7
0.2
0.0001
structural molecule
AF381029
LMNA
0.3
0.00001
structural molecule
X83929
DSC3
0.009
0.0035
cell adhesion
AB025105
CDH1
0.01
0.0009
cell adhesion
AJ246000
SELL
0.21
0.002
cell adhesion
NM_003568
ANXA9
0.22
0.0031
cell adhesion
AF281287
PECAM1
0.36
0.0017
cell adhesion
J00124
KRT14
0.0001
0.0003
cytoskeleton/motility
BC034535
KRT6B
0.005
0.0043
cytoskeleton/motility
M19156
KRT10
0.018
0.001
cytoskeleton/motility
AJ551176
SDC1
0.039
0.0038
cytoskeleton/motility
NM_006478
GAS2L1
0.22
0.0016
cytoskeleton/motility
M34225
KRT8
0.26
0.0029
cytoskeleton/motility
NM_005886
KATNB1
0.27
0.0011
cytoskeleton/motility
AK024835
CNN2
0.47
0.003
cytoskeleton/motility
NM_006350
FST
0.11
0.00001
extracellular matrix
AF177941
COLSA3
0.14
0.00001
extracellular matrix
L22548
COL18A1
0.49
0.0011
extracellular matrix
M58051
FGFR3
0.007
0.0039
growth factor receptor
NM_004887
CXCL14
0.009
0.0014
chemokine
AF289090
BMP7
0.13
0.002
cytokine
K03222
TGFA
0.2
0.0048
growth factor
M31682
INHBB
0.20
0.00001
cytokine
NM_004750
CRLF1
0.26
0.0003
cytokine binding
NM_002514
NOV (CCN3)
0.28
0.0009
growth factor
NM_000685
AGTR1
0.30
0.005
growth factor receptor
D16431
HDGF
0.42
0.0046
creatine kinase
L36719
MAP2K3
0.22
0.0048
protein kinase activity
AJ290975
ITPKC
0.28
0.0036
protein kinase activity
NM_001569
IRAK1
0.33
0.0001
protein kinase activity
AB025285
ERBB2
0.45
0.0003
protein kinase
AF029082
SFN
0.001
0.0028
signal transduction
AB065865
HM74
0.04
0.0047
signal transduction
AA021034
LTB4R
0.06
0.0006
signal transduction
NM_004445
EPHB6
0.12
0.0038
signal transduction
AF025304
EPHB2
0.17
0.0021
signal transduction
AB026663
MC1R
0.17
0.0046
signal transduction
AF035442
VAV3
0.17
0.004
signal transduction
NM_014030
GIT1
0.21
0.0025
signal transduction
AB011152
CENTD1
0.21
0.0003
signal transduction
AK095244
CYB561
0.23
0.0001
signal transduction
AF106858
GPR56
0.23
0.0002
signal transduction
AF231024
CELSR1
0.23
0.0006
signal transduction
AF234887
CELSR2
0.24
0.0003
signal transduction
NM_007197
FZD10
0.25
0.0009
signal transduction
NM_014349
APOL3
0.25
0.002
signal transduction
NM_004039
ANXA2
0.27
0.0044
signal transduction
AI285986
THBD
0.29
0.0004
signal transduction
M57730
EFNA1
0.31
0.0032
signal transduction
NM_002118
HLA-DMB
0.33
0.0008
signal transduction
AF427491
TUBB4
0.36
0.001
signal transduction
NM_005279
GPR1
0.40
0.0033
signal transduction
X60592
TNFRSF5
0.40
0.0032
signal transduction
BC052968
EPHB3
0.42
0.0001
signal transduction
M64749
CMKOR1
0.46
0.0014
signal transduction
M21188
IDE
0.46
0.0031
signal transduction
AB018325
CENTD2
0.47
0.0004
signal transduction
AK054968
ITGB5
0.49
0.0005
signal transduction
NM_001730
KLF5
0.04
0.0021
transcription factor
NM_004350
RUNX3
0.08
0.0001
transcription factor
U34070
CEBPA
0.11
0.0005
transcription factor
AF062649
PTTG1
0.15
0.0039
transcription factor
NM_004235
KLF4
0.20
0.0005
transcription factor
X52773
RXRA
0.20
0.0011
transcription factor
AF202118
HOXD1
0.21
0.0006
transcription factor
NM_000376
VDR
0.21
0.0001
transcription factor
NM_006548
IMP-2
0.26
0.0031
transcription factor
NM_007315
STAT1
0.32
0.00001
transcription factor
NM_004430
EGR3
0.34
0.002
transcription factor
NM_003644
GAS7
0.36
0.0033
transcription factor
NM_005900
MADH1
0.48
0.0028
transcription factor
X14454
IRF1
0.49
0.0013
transcription factor
AF067572
STAT6
0.49
0.0001
transcription factor
NM_005596
NFIB
0.49
0.0041
transcription factor
AB002282
EDF1
0.40
0.0002
transcription coactivator
AK075393
CTSB
0.50
0.0016
protease activity
AB021227
MMP24
0.29
0.0001
protease activity
AB007774
CSTA
0.02
0.0018
cysteine protease inhibitor
AF143883
ALOX12
0.06
0.0016
catalytic activity
AF440204
PTGS1
0.08
0.00001
catalytic activity
NM_000777
CYP3A5
0.14
0.0041
catalytic activity
NM_016593
CYP39A1
0.21
0.0027
catalytic activity
BC001491
HMOX1
0.23
0.0028
catalytic activity
BC020734
PGDS
0.26
0.00001
catalytic activity
AL133324
GSS
0.39
0.002
catalytic activity
AF055027
CARM1
0.41
0.00001
catalytic activity
NM_001630
ANXA8
0.01
0.0006
calcium ion binding
AB011542
EGFL5
0.43
0.0001
calcium ion binding
NM_005979
S100A13
0.31
0.001
calcium ion binding
NM_020672
S100A14
0.02
0.0005
calcium ion binding
NM_005978
S100A2
0.003
0.005
calcium ion binding
BC012610
HF1
0.22
0.00001
complement activation
AF052692
GJB3
0.03
0.0001
connexon channel activity
M12529
APOE
0.21
0.0001
metabolism
NM_004925
AQP3
0.01
0.0003
transporter activity
Partial list of differentially expressed genes identified in leiomyomas (African Americans and Caucasians) as compared to keloid/incisional scars as shown in Fig. 1. The genes were selected based on p ranking of p ≤ 0.005 and 2-fold cutoff change selection (F. Change) as described in materials and methods. Table 2 displays the under-expressed genes in leiomyomas as compared to keloid/incisional scars.
The analysis based on inclusion of leiomyomas as two independent cohorts (3 A. American and 3 Caucasians) resulted in identification of a limited number of differentially expressed genes as compared to keloids (N = 2)/incisional scars (N = 2). Because both keloids were from A. American patients we excluded one of the incisional scar from a Caucasian patient from the analysis and lowered the statistical stringency to P < 0.01 which resulted in identified 424 differentially expressed genes in A. American leiomyomas as compared to keloids/scars. Similar analysis resulted in identified 393 differentially expressed genes in Caucasian leiomyomas as compared to keloids/scars (all from A. Americans). Of these genes 64 and 32 genes, respectively differed by at least 2 fold in leiomyomas of AA and Caucasians, compared to keloids/incisional scars (Table 3).
Table 3
Differentially expressed genes in leiomyomas compared to keloids/incesional scars
Gene Bank
Symbol
F. Change
LAA:Scar
F. Change
LC:Scar
P value
Function
NM_006198
PCP4
68.14
6.66
0.0017
system development
S67238
MYOSIN
62.78
36.69
0.0034
cytoskeleton/motility
NM_004342
Cald1
21.43
9.32
0.0047
cytoskeleton/motility
NM_013437
LRP12
20.6
6.82
0.0053
cellular process
AC004010
AMIGO2
19.07
10.61
0.0021
cell adhesion
AF040254
OCX
18.71
5.39
0.0099
signal transduction
NM_015385
SORBS1
17.44
9.26
0.0003
cytoskeleton/motility
NM_012278
ITGB1BP2
17.42
9.9
0.0018
signal transduction
NM_006101
KNTC2
17.33
5.23
0.0022
transcription factor
NM_001845
COL4A1
16.08
5.94
0.0029
cytoskeleton/motility
AF104857
CDC42EP3
16.08
3.78
0.0002
cytoskeleton/motility
AW188131
DDX17
15.65
9.11
0.0005
translation factor
NM_001057
TACR2
15.6
4.51
0.0062
signal transduction
AI375002
ZNF447
14.55
8.04
0.0061
transcription factor
NM_014890
DOC1
14.35
5.19
0.0002
proteolysis
NM_001784
CD97
13.16
6.35
0.00004
signal transduction
BF111821
WSB1
12.34
7.36
0.0024
signal transduction
AW152664
PNN
12.19
8.26
0.003
transcription factor
NM_002380
MATN2
11.86
5.62
0.0011
extracellular matrix
NM_007362
NCBP2
11.38
8.04
0.0034
RNA processing
AK023406
Macf1
8.8
4.77
0.0041
ECM signaling
AF095192
BAG2
8.01
4.34
0.0018
apoptosis
NM_004196
CDKL1
7.91
2.83
0.0017
cell cycle
BF512200
MBNL2
7.58
3.01
0.0014
muscle differentiaon
AW043713
Sulfl
6.9
0.78
0.0039
hydrolase activity
NM_004781
VAMP3
6.76
3.02
0.0016
trafficking
AI149535
STAT5B
5.62
3.94
0.0043
transcription factor
NM_016277
RAB23
5.61
2.68
0.0055
signal transduction
AI582238
TRA1
5.13
3.46
0.0042
calcium ion binding
NM_005722
ACTR2
4.04
2.49
0.0001
cytoskeleton/motility
AF016005
RERE
4.02
2.87
0.008
transcription factor
AL046979
TNS1
3.65
2.14
0.0047
signal transduction
NM_005757
MBNL2
3.57
0.84
0.0049
muscle development
AJ133768
LDB3
3.3
1.53
0.0056
cytoskeleton/motility
AI650819
CUL4B
3.04
1.59
0.0045
metabolism
AL031602
MT1K
0.61
0.33
0.0086
cadmium ion binding
U85658
TFAP2C
0.27
0.14
0.0083
transcription factor
NM_003790
TNFRSF25
0.19
0.11
0.007
apoptosis
BC002495
BAIAP2
0.18
0.11
0.0003
signal transduction
AV691491
TMEM30B
0.13
0.09
0.0093
cell cycle control
AI889941
COL4A6
10.4
30.21
0.007
extracellular matrix
AW451711
PBX1
14.44
18.14
0.0001
transcription factor
NM_014668
GREB1
7.18
15.94
0.0089
 
NM_004619
TRAF5
6.47
11.46
0.0091
signal transduction
NM_005418
ST5
5.83
8.1
0.0044
signal transduction
BC002811
SUMO2
0.47
0.83
0.0035
protein binding
AV700891
ETS2
0.28
0.54
0.0082
transcription factor
AB042557
PDE4DIP
0.2
0.39
0.0019
signaling
NM_014485
PGDS
0.17
0.31
0.0027
catalytic activity
AI984221
COL5A3
0.08
0.17
0.0011
extracellular matrix
NM_006823
PKIA
0.08
0.17
0.0034
Kinase regulator
AU144284
IRF6
0.04
0.15
0.0026
transcription factor
NM_000962
PTGS1
0.06
0.11
0.0046
catalytic activity
NM_022898
BCL11B
0.05
0.09
0.0099
transcription factor
NM_001982
ERBB3
0.02
0.06
0.0066
signal transduction
NM_002705
PPL
0.005
0.031
0.0073
hydrolase activity
NM_001630
ANXA8
0.006
0.02
0.0079
calcium ion binding
N74607
AQP3
0.006
0.02
0.0098
transporter activity
NM_000142
FGFR3
0.007
0.009
0.01
Growth factor
Receptor
     
Partial list of differentially expressed genes from several functional categories in leiomyomas from African Americans and Caucasians as compared to keloids/incesional scars as shown in Fig. 2. The genes were selected based on p ranking of p ≤ 0.01 and following 2-fold cutoff change
We also utilized the gene expression values obtained in our previous microarray studies in leiomyomas[11] and peritoneal adhesions (unpublished results) for comparative analysis. Because these results were generated using Affymetrix U95A GeneChips, due to cross-platform comparability with U133A the combined data from both platforms were subjected to additional analysis as described in the materials and methods. The analysis based on p < 0.005 and 2-fold change cutoff identified 1801 genes as over-expressed and 45 under-expressed in leiomyomas as compared to keloids/incisional scars and peritoneal adhesions (considered as one cohort during analysis). Of these, 85 genes were differentially expressed in leiomyomas as compared to peritoneal adhesions (Fig. 2), however exclusion of U133A data from the analysis resulted in identification of a higher number differentially expressed genes. The gene expression profiles in these tissues were comparatively analyzed with their corresponding normal tissues, myometrium, skin and peritoneum, and as expected they displayed distinct patterns (data not shown). The analysis confirmed the effect of cross-platform on gene expression profiling when comparing results of different studies (See Nature Bio-technology, Sept 2006 for several reviews).

Realtime PCR of gene expression

Gene ontology assessment and division into functional categories indicated that a majority of the differentially expressed genes identified in these cohorts serve as regulator of transcription, cell cycle and apoptosis, extracellular matrix turnover, adhesion molecules, signal transduction and transcription factors (Tables 1, 2 and 3). Since the expression of E2F1, RUNX3, EGR3, TBPIP, ECM-2, ESM1, THBS1, GAS1, ADAM17, CST6, FBLN5, and COL18A1 was evaluated in leiomyomas using LDA-based realtime PCR as described in the accompanying manuscript [17] we used the same approach and compared their expression in keloids, incisional scars and peritoneal adhesions. The level of expression of these 12 genes displayed significant variations among these tissues with some overlapping patterns with the microarray results. By setting the mean expression value of each gene independently as 1 in leiomyomas compared with their mean expression in keloids/incisional scars (scar) and adhesions, the results indicated that the expression of E2F1, TBPIP and ESM1 was elevated in leiomyoma as compared to keloids/incisional scars and adhesions (Fig. 3, P < 0.05). In contrast, the expression of EGR3, ECM2, THBS1, GAS1 and FBLN5 in scars and RUNX3 and COL18 expression in peritoneal adhesions was higher as compared to leiomyomas (Fig. 3).

Discussion

Using a large-scale gene expression profiling approach we compared leiomyomas with keloids, incisional cars and peritoneal adhesions and found that their molecular environments consist of a combination of both tissue-specific and commonly expressed genes. The tissue-specific gene expression between leiomyomas and keloids was not reflected based on the presence/absence of unique genes, but rather occurred at the level of expression of a selective number of differentially expressed genes. As such an elevated level of expression of a number of muscle cell-specific genes in leiomyomas and fibroblast-specific genes in keloids reflected the specific cellular make up of these tissues. In addition, specific expression of estrogen receptor (ER) in leiomyomas with limited expression in keloids and incesional scar tissues re-enforced the importance of ovarian steroids in leiomyomas growth. Collectively the results suggest that the molecular environments that govern the characteristic of these fibrotic tissues, at least at genomic levels, are relatively similar and involved specific set of genes represented by 3 to 12% of the genes on the array. This observation also suggests that differential expression of a limited number of these genes with unique biological functions may regulate the processes that results in establishment and progression of leiomyoma, keloids, incisional scars, and possibly other fibrotic disorders, despite differences in the nature of their development and growth.
We recognize that the stage of the menstrual cycle and to a limited extend the size of leiomyomas, as well as the period since keloids, incisional scars and peritoneal adhesions were first formed, reflecting the stage of wound healing, influences the outcome of their gene expression. Although leiomyomas used in our study were similar in size and from the same phase of the menstrual cycle, the stage of keloids and scars tissues was unknown. As such the study results represent their gene expression at the time of collection. We also recognize that small sample size limited our ability to analyze the data based on ethnicity, because of more frequent development of leiomyomas and keloids in African Americans. However, it is worth mentioning that comparing leiomyomas with keloids from this ethnic group showed a limited difference in their gene expression profile, or when compared with leiomyomas from Caucasians, suggesting the existence of a comparable environment in leiomyomas and keloids. Further comparison of leiomyomas' gene expression with peritoneal adhesions (Affymetrix U95A subjected to cross-platform comparability analysis) also identified a low number of differentially expressed genes (85 genes) in these tissues, although analysis based only on U95A arrays identified higher numbers. The results indicate that the molecular environment of leiomyomas may be more comparable to peritoneal adhesions as compared to keloids/incisional scars at least at late stage of their wound healing development. Possibly the size of leiomyomas (larger size often undergoing degeneration at the center), and the stage of keloids, incesional scars and adhesions formation following tissue injury influencing their gene expression profiles would produce different results from our study and their evaluation would enhance our understanding of molecular conditions that lead to tissue fibrosis at these and other sites [1821].
A majority of the genes identified in leiomyomas, keloid, incisional scars and adhesions function as regulators of cell survival (cell cycle and apoptosis), cell and tissue structure (ECM, adhesion molecules and cytoskeleton), tissue turnover, inflammatory mediators, signal transduction and transcription and metabolism. Consistent with the importance of ECM, cytoskeleton, adhesion molecules and proteases in tissue fibrosis we identified the expression of many of genes in these categories some with 5 to 60 fold increase in their expression. Elevated expression of DES, MYH11, MYL9 and SMTN in leiomyomas and several KRTs in keloids and scars reflects the cellular composition of these tissues. Additionally, PALLD has been considered to serve as a novel marker of myofibroblast conversion and is regulated by profibrotic cytokine such as TGF-β [22, 23]. SM22, which is overexpressed in keloids[24], promotes ECM accumulation through inhibition of MMP-9 expression [25]. The expression of many components of ECM including collagens, decorin, versican, fibromodulin, intergrins, extracellular matrix protein 1 (ECM-1), syndecan and ESM-1 has been identified in leiomyomas [11, 17, 26] as well as dermal wounds during healing, scars and keloids (for review see [2732]).
We validated the expression of ECM-2, ESM1, THBS1, FBLN5 and COL18A1 in keloids, incisional scars and adhesions and the analysis indicated an elevated expression of ECM2, THBS1 and FBLN5 in keloid/incisional scars and COL18 in peritoneal adhesions as compared to leiomyomas[17]. Although the biological significance of these gene products and changes in their expression in leiomyomas, keloids and adhesions remains to be established, the product of a specific number of these genes such as ECMs, THBS1, FBLNs, MMPs and ADAMs play a critical role in various aspect of wound healing and tissue fibrosis [2732]. A number of MMPs were equally expressed in leiomyomas, keloids and peritoneal adhesions with the exception of lower MMP-14, MMP-24 and MMP-28 expression in leiomyomas, suggesting that these tissues are potential target of their proteolytic actions. The biological importance of lower expression of these MMPs in leiomyoma is unknown; however unlike most MMPs that are secreted as inactive proenzymes and require activation, MMP-11 and MMP-28 are secreted in active forms. In keratinocytes, MMP-28 is expressed in response to injury and detected in the conditioned media of hypertrophic scars, but not normotrophic scars [33]. A lower expression of MMP-28 and elevated expression of TIMP-3 in leiomyomas compared to keloids imply a lower matrix turnover with an increase angiogenic and pro-apoptotic activities that has been associated with TIMP-3 [34, 35].
We identified an overexpression of a higher number of apoptotic-related genes in keloids and incisional scars as compared to leiomyomas, suggesting an increased rate of cellular turnover. Because apoptotic and non-apoptotic cell death is considered to increase local inflammatory reaction and a key step in tissue fibrosis, a number of genes functionally categorized as proinflammatory and pro-fibrotic mediators were identified in these tissues. Noticeable among these genes were TGF-β, IL-1, IL-6, IL-11, IL-13, IL-17, IL-22 and IL-27 and chemokines CCL-2 to 5, CX3-CL1, CXCL-1, CXCL-12 and CXCL-14 and their receptors. Elevated expression of PDGF-C, VEGF and FGF2 in leiomyomas as compared to keloids and adhesions imply an additional role for these angiogenic factors in pathogenesis of leiomyomas. While the expression of TGF-β was equally elevated in leiomyomas, keloids, incisional scars and peritoneal adhesion as compared to their normal tissues reinforcing the importance of TGF-β as principle mediator of tissue fibrosis [30]. Although profibrotic action of TGF-β is reported to involve the induction of CTGF, a member of PDGF family with mitogen action for myofibroblasts [36], it is expressed at lower levels in leiomyomas as compared to myometrium [26, 37, 38]. However, leiomyomas of African Americans expressed a 3.3 fold higher levels of CTGF as compared to Caucasians, and 12.6 and 4.3 fold higher as compared to keloids and incisional scars, respectively. Although the biological significance of these differences needs further investigation, altered expression of many of these genes as compared to their normal tissues counterpart also imply their potential role in various cellular processes that results in tissue fibrosis.
The genes encoding signal transduction and transcription factors represented the largest functional category in leiomyomas and scar tissues. They included several genes such as NR2F1, PNN, Smad4, Smad5, STAT5B, JUN, TGIF2, and ATF1 that were over-expressed while RUNX3, STAT1, STAT6, EGR3, GAS7, Smad1, and EDF1 were underexpressed in leiomyomas as compared to keloid/incisional scars. We validated the expression of E2F1, RUNX3, EGR3 and TBPIP in leiomyomas [17], keloids, incisional scars and peritoneal adhesions showing a good correlation with microarray data Since activation of these signal transduction pathways and transcription factors regulate the expression of large number of genes with diverse functional activities their altered expression in these tissues could have a considerably more important role in tissue fibrosis than previously considered. Preferential phosphorylation of many of these transcription factors such as Jun, Stats, Smads, Runx and EGRs leads to regulation of target genes involved in cell growth and apoptosis, inflammation, angiogenesis and tissue turnover with central roles in tissue fibrosis [11, 17, 3942]
In conclusion, the gene expression profiling involving leiomyomas and their comparison with keloids, incisional scars and peritoneal adhesion indicated that a combination of tissue-specific and common genes differentiate their molecular environments. The tissue-specific differences were not based on the presence/absence of unique genes, but rather the level of expression of selective number of genes accounting for 3 to 12% of the genes on the array. Although the nature of leiomyomas' development and growth is vastly different from these fibrotic tissues, we speculate that the outcome of their tissue characteristics is influenced by the products of genes regulating cell growth and apoptosis, inflammation, angiogenesis and tissue turnover, and may also be under different tissue-specific regulatory control.

Acknowledgements

We thank Dr. Mick Popp at Interdisciplinary Center for Biotechnology Research at the University of Florida for assistance with microarray chip analysis. The work presented here is supported by a grant HD37432 from the National Institute of Health. The work was presented in part at the 53 rd Annual Meeting of the Society for Gynecological Investigation, Reno NA, and March 2007.
Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://​creativecommons.​org/​licenses/​by/​2.​0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Competing interests

The author(s) declare that they have no competing interests.

Authors' contributions

XL, QP and NC participated in all aspect of the experimental design and writing of the work presented here. The final microarray gene chips were performed at Interdisciplinary Center for Biotechnology Research at the University of Florida. The analysis of microarray gene expression profiles between the gene chips U95 and 133a was carried out by LL and gene expression analysis and realtime PCR was performed by XL and QP. All the authors read and approved the final manuscript.
Anhänge

Authors’ original submitted files for images

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Metadaten
Titel
Genomic and proteomic profiling II: Comparative assessment of gene expression profiles in leiomyomas, keloids, and surgically-induced scars
verfasst von
Xiaoping Luo
Qun Pan
Li Liu
Nasser Chegini
Publikationsdatum
01.12.2007
Verlag
BioMed Central
Erschienen in
Reproductive Biology and Endocrinology / Ausgabe 1/2007
Elektronische ISSN: 1477-7827
DOI
https://doi.org/10.1186/1477-7827-5-35

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